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Li J, Li X, Chen F, Li W, Chen J, Zhang B. Studying the Alzheimer's disease continuum using EEG and fMRI in single-modality and multi-modality settings. Rev Neurosci 2024; 35:373-386. [PMID: 38157429 DOI: 10.1515/revneuro-2023-0098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 12/01/2023] [Indexed: 01/03/2024]
Abstract
Alzheimer's disease (AD) is a biological, clinical continuum that covers the preclinical, prodromal, and clinical phases of the disease. Early diagnosis and identification of the stages of Alzheimer's disease (AD) are crucial in clinical practice. Ideally, biomarkers should reflect the underlying process (pathological or otherwise), be reproducible and non-invasive, and allow repeated measurements over time. However, the currently known biomarkers for AD are not suitable for differentiating the stages and predicting the trajectory of disease progression. Some objective parameters extracted using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are widely applied to diagnose the stages of the AD continuum. While electroencephalography (EEG) has a high temporal resolution, fMRI has a high spatial resolution. Combined EEG and fMRI (EEG-fMRI) can overcome single-modality drawbacks and obtain multi-dimensional information simultaneously, and it can help explore the hemodynamic changes associated with the neural oscillations that occur during information processing. This technique has been used in the cognitive field in recent years. This review focuses on the different techniques available for studying the AD continuum, including EEG and fMRI in single-modality and multi-modality settings, and the possible future directions of AD diagnosis using EEG-fMRI.
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Affiliation(s)
- Jing Li
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Xin Li
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Futao Chen
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Weiping Li
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Jiu Chen
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Bing Zhang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
- Jiangsu Key Laboratory of Molecular Medicine, Nanjing, Jiangsu, 210008, China
- Institute of Brain Science, Nanjing University, Nanjing, Jiangsu, 210008, China
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2
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Awarayi NS, Twum F, Hayfron-Acquah JB, Owusu-Agyemang K. A bilateral filtering-based image enhancement for Alzheimer disease classification using CNN. PLoS One 2024; 19:e0302358. [PMID: 38640105 PMCID: PMC11029622 DOI: 10.1371/journal.pone.0302358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 04/03/2024] [Indexed: 04/21/2024] Open
Abstract
This study aims to develop an optimally performing convolutional neural network to classify Alzheimer's disease into mild cognitive impairment, normal controls, or Alzheimer's disease classes using a magnetic resonance imaging dataset. To achieve this, we focused the study on addressing the challenge of image noise, which impacts the performance of deep learning models. The study introduced a scheme for enhancing images to improve the quality of the datasets. Specifically, an image enhancement algorithm based on histogram equalization and bilateral filtering techniques was deployed to reduce noise and enhance the quality of the images. Subsequently, a convolutional neural network model comprising four convolutional layers and two hidden layers was devised for classifying Alzheimer's disease into three (3) distinct categories, namely mild cognitive impairment, Alzheimer's disease, and normal controls. The model was trained and evaluated using a 10-fold cross-validation sampling approach with a learning rate of 0.001 and 200 training epochs at each instance. The proposed model yielded notable results, such as an accuracy of 93.45% and an area under the curve value of 0.99 when trained on the three classes. The model further showed superior results on binary classification compared with existing methods. The model recorded 94.39%, 94.92%, and 95.62% accuracies for Alzheimer's disease versus normal controls, Alzheimer's disease versus mild cognitive impairment, and mild cognitive impairment versus normal controls classes, respectively.
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Affiliation(s)
- Nicodemus Songose Awarayi
- Department of Computer Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
- Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani, Ghana
| | - Frimpong Twum
- Department of Computer Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - James Ben Hayfron-Acquah
- Department of Computer Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Kwabena Owusu-Agyemang
- Department of Computer Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
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Yao Z, Wang H, Yan W, Wang Z, Zhang W, Wang Z, Zhang G. Artificial intelligence-based diagnosis of Alzheimer's disease with brain MRI images. Eur J Radiol 2023; 165:110934. [PMID: 37354773 DOI: 10.1016/j.ejrad.2023.110934] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 05/21/2023] [Accepted: 06/15/2023] [Indexed: 06/26/2023]
Abstract
Alzheimer's disease, a primary neurodegenerative condition, predominantly impacts the elderly and pre-elderly population. This progressive neurological disorder is characterized by an array of symptoms including memory loss, cognitive decline, and various physiological and psychological disturbances, significantly compromising the quality of life of patients and their caregivers. Recent advancements in Magnetic Resonance Imaging (MRI) technology have catalyzed research in AI-enhanced diagnostics for Alzheimer's disease, fostering optimism for early detection and timely interventions. This progress has paved the way for the development of sophisticated algorithms and models adept at analyzing complex brain imaging data, thereby augmenting diagnostic accuracy and efficiency. This advancement fuels optimism regarding the transformative potential of AI-driven diagnostics in revolutionizing Alzheimer's disease management, with the prospect of facilitating more effective treatment strategies and improved patient outcomes. The objective of this review is to provide a comprehensive overview of recent developments in deep learning methodologies applied to brain MRI images for the classification of various stages of Alzheimer's disease, with a particular emphasis on early diagnosis. Furthermore, this review underscores the limitations of current research, discussing potential challenges and future research directions in this dynamic field.
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Affiliation(s)
- Zhaomin Yao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China; Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning 110016, China
| | - Hongyu Wang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China; Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning 110016, China
| | - Wencheng Yan
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China
| | - Zheling Wang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China
| | - Wenwen Zhang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China; Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning 110016, China
| | - Zhiguo Wang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China; Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning 110016, China.
| | - Guoxu Zhang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China; Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning 110016, China.
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Sibilano E, Brunetti A, Buongiorno D, Lassi M, Grippo A, Bessi V, Micera S, Mazzoni A, Bevilacqua V. An attention-based deep learning approach for the classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG. J Neural Eng 2023; 20. [PMID: 36745929 DOI: 10.1088/1741-2552/acb96e] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 02/06/2023] [Indexed: 02/08/2023]
Abstract
Objective. This study aims to design and implement the first deep learning (DL) model to classify subjects in the prodromic states of Alzheimer's disease (AD) based on resting-state electroencephalographic (EEG) signals.Approach. EEG recordings of 17 healthy controls (HCs), 56 subjective cognitive decline (SCD) and 45 mild cognitive impairment (MCI) subjects were acquired at resting state. After preprocessing, we selected sections corresponding to eyes-closed condition. Five different datasets were created by extracting delta, theta, alpha, beta and delta-to-theta frequency bands using bandpass filters. To classify SCDvsMCI and HCvsSCDvsMCI, we propose a framework based on the transformer architecture, which uses multi-head attention to focus on the most relevant parts of the input signals. We trained and validated the model on each dataset with a leave-one-subject-out cross-validation approach, splitting the signals into 10 s epochs. Subjects were assigned to the same class as the majority of their epochs. Classification performances of the transformer were assessed for both epochs and subjects and compared with other DL models.Main results. Results showed that the delta dataset allowed our model to achieve the best performances for the discrimination of SCD and MCI, reaching an Area Under the ROC Curve (AUC) of 0.807, while the highest results for the HCvsSCDvsMCI classification were obtained on alpha and theta with a micro-AUC higher than 0.74.Significance. We demonstrated that DL approaches can support the adoption of non-invasive and economic techniques as EEG to stratify patients in the clinical population at risk for AD. This result was achieved since the attention mechanism was able to learn temporal dependencies of the signal, focusing on the most discriminative patterns, achieving state-of-the-art results by using a deep model of reduced complexity. Our results were consistent with clinical evidence that changes in brain activity are progressive when considering early stages of AD.
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Affiliation(s)
- Elena Sibilano
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Via Orabona 4, 70125 Bari, Italy
| | - Antonio Brunetti
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Via Orabona 4, 70125 Bari, Italy
| | - Domenico Buongiorno
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Via Orabona 4, 70125 Bari, Italy
| | - Michael Lassi
- The BioRobotics Institute, Scuola Superiore Sant'Anna, 56025 Pisa, Italy
| | | | - Valentina Bessi
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Azienda Ospedaliera Careggi, Florence, Italy
| | - Silvestro Micera
- The BioRobotics Institute, Scuola Superiore Sant'Anna, 56025 Pisa, Italy.,Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics, Institute of Bioengineering, School of Engineering, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Alberto Mazzoni
- The BioRobotics Institute, Scuola Superiore Sant'Anna, 56025 Pisa, Italy
| | - Vitoantonio Bevilacqua
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Via Orabona 4, 70125 Bari, Italy
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Parreño Torres A, Roncero-Parra C, Borja AL, Mateo-Sotos J. Inter-Hospital Advanced and Mild Alzheimer's Disease Classification Based on Electroencephalogram Measurements via Classical Machine Learning Algorithms. J Alzheimers Dis 2023; 95:1667-1683. [PMID: 37718814 DOI: 10.3233/jad-230525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
BACKGROUND In pursuit of diagnostic tools capable of targeting distinct stages of Alzheimer's disease (AD), this study explores the potential of electroencephalography (EEG) combined with machine learning (ML) algorithms to identify patients with mild or moderate AD (ADM) and advanced AD (ADA). OBJECTIVE This study aims to assess the classification accuracy of six classical ML algorithms using a dataset of 668 patients from multiple hospitals. METHODS The dataset comprised measurements obtained from 668 patients, distributed among control, ADM, and ADA groups, collected from five distinct hospitals between 2011 and 2022. For classification purposes, six classical ML algorithms were employed: support vector machine, Bayesian linear discriminant analysis, decision tree, Gaussian Naïve Bayes, K-nearest neighbor and random forest. RESULTS The RF algorithm exhibited outstanding performance, achieving a remarkable balanced accuracy of 93.55% for ADA classification and 93.25% for ADM classification. The consistent reliability in distinguishing ADA and ADM patients underscores the potential of the EEG-based approach for AD diagnosis. CONCLUSIONS By leveraging a dataset sourced from multiple hospitals and encompassing a substantial patient cohort, coupled with the straightforwardness of the implemented models, it is feasible to attain notably robust results in AD classification.
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Affiliation(s)
| | | | - Alejandro L Borja
- School of Industrial Engineering, University of Castilla-La Mancha, Albacete, Spain
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Kumar A, Singh Sodhi S. Classification of data on stacked autoencoder using modified sigmoid activation function. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-212873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
A Neural Network is one of the techniques by which we classify data. In this paper, we have proposed an effectively stacked autoencoder with the help of a modified sigmoid activation function. We have made a two-layer stacked autoencoder with a modified sigmoid activation function. We have compared our autoencoder to the existing autoencoder technique. In the existing autoencoder technique, we generally use the logsigmoid activation function. But in multiple cases using this technique, we cannot achieve better results. In that case, we may use our technique for achieving better results. Our proposed autoencoder may achieve better results compared to this existing autoencoder technique. The reason behind this is that our modified sigmoid activation function gives more variations for different input values. We have tested our proposed autoencoder on the iris, glass, wine, ovarian, and digit image datasets for comparison propose. The existing autoencoder technique has achieved 96% accuracy on the iris, 91% accuracy on wine, 95.4% accuracy on ovarian, 96.3% accuracy on glass, and 98.7% accuracy on digit (image) dataset. Our proposed autoencoder has achieved 100% accuracy on the iris, wine, ovarian, and glass, and 99.4% accuracy on digit (image) datasets. For more verification of the effeteness of our proposed autoencoder, we have taken three more datasets. They are abalone, thyroid, and chemical datasets. Our proposed autoencoder has achieved 100% accuracy on the abalone and chemical, and 96% accuracy on thyroid datasets.
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Affiliation(s)
- Arvind Kumar
- Computer Science, and Enginering USICT GGSIPU, Delhi
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7
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Han J, Jiang G, Ouyang G, Li X. A Multimodal Approach for Identifying Autism Spectrum Disorders in Children. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2003-2011. [PMID: 35853070 DOI: 10.1109/tnsre.2022.3192431] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Identification of autism spectrum disorder (ASD) in children is challenging due to the complexity and heterogeneity of ASD. Currently, most existing methods mainly rely on a single modality with limited information and often cannot achieve satisfactory performance. To address this issue, this paper investigates from internal neurophysiological and external behavior perspectives simultaneously and proposes a new multimodal diagnosis framework for identifying ASD in children with fusion of electroencephalogram (EEG) and eye-tracking (ET) data. Specifically, we designed a two-step multimodal feature learning and fusion model based on a typical deep learning algorithm, stacked denoising autoencoder (SDAE). In the first step, two SDAE models are designed for feature learning for EEG and ET modality, respectively. Then, a third SDAE model in the second step is designed to perform multimodal fusion with learned EEG and ET features in a concatenated way. Our designed multimodal identification model can automatically capture correlations and complementarity from behavior modality and neurophysiological modality in a latent feature space, and generate informative feature representations with better discriminability and generalization for enhanced identification performance. We collected a multimodal dataset containing 40 ASD children and 50 typically developing (TD) children to evaluate our proposed method. Experimental results showed that our proposed method achieved superior performance compared with two unimodal methods and a simple feature-level fusion method, which has promising potential to provide an objective and accurate diagnosis to assist clinicians.
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8
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Saravanakumar S, Saravanan T. An effective convolutional neural network-based stacked long short-term memory approach for automated Alzheimer’s disease prediction. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-212797] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
In today’s world, Alzheimer’s Disease (AD) is one of the prevalent neurological diseases where early disease prediction can significantly enhance the compatibility of patient treatment. Nevertheless, accurate diagnosis and optimal feature selection play a vital challenge in AD detection. Most of the existing diagnosis systems failed to attain superior prediction accuracy and precision rate. In order to mitigate these constraints, a new efficient Convolutional Neural Network-based Stacked Long Short-Term Memory (CNN-SLSTM) methodology has been proposed in this paper. The key objective of the proposed model is to examine the brain’s condition and evaluate the changes that occur throughout the interracial period. The proposed model includes multi-feature learning and categorization in which the raw Electroencephalography (EEG) data will be passed via the feature extractor to decrease the computing complexity and execution time. Afterward, the SLSTM network is constructed with completely linked layer and activation layers to record the temporal relationship between features and the next stage of AD. The proposed CNN-SLSTM model can be trained using real-time EEG sensor data. The performance results clearly apparent that the proposed model can efficiently predict the AD with superior accuracy of 98.67% and precision of 98.86% when compared with existing state-of-the-art techniques.
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Affiliation(s)
- S. Saravanakumar
- Department of Computer Science and Engineering, Adithya Institute of Technology, Coimbatore, India
| | - T. Saravanan
- Department of Computer Science and Engineering, St Martins Engineering college Telangana, India
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9
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García Pretelt FJ, Suárez Relevo JX, Aguillón D, Lopera F, Ochoa JF, Tobón Quintero CA. Automatic Classification of Subjects of the PSEN1-E280A Family at Risk of Developing Alzheimer’s Disease Using Machine Learning and Resting State Electroencephalography. J Alzheimers Dis 2022; 87:817-832. [DOI: 10.3233/jad-210148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: The study of genetic variant carriers provides an opportunity to identify neurophysiological changes in preclinical stages. Electroencephalography (EEG) is a low-cost and minimally invasive technique which, together with machine learning, provide the possibility to construct systems that classify subjects that might develop Alzheimer’s disease (AD). Objective: The aim of this paper is to evaluate the capacity of the machine learning techniques to classify healthy Non-Carriers (NonCr) from Asymptomatic Carriers (ACr) of PSEN1-E280A variant for autosomal dominant Alzheimer’s disease (ADAD), using spectral features from EEG channels and brain-related independent components (ICs) obtained using independent component analysis (ICA). Methods: EEG was recorded in 27 ACr and 33 NonCr. Statistical significance analysis was applied to spectral information from channels and group ICA (gICA), standardized low-resolution tomography (sLORETA) analysis was applied over the IC as well. Strategies for feature selection and classification like Chi-square, mutual informationm and support vector machines (SVM) were evaluated over the dataset. Results: A test accuracy up to 83% was obtained by implementing a SVM with spectral features derived from gICA. The main findings are related to theta and beta rhythms, generated in the parietal and occipital regions, like the precuneus and superior parietal lobule. Conclusion: Promising models for classification of preclinical AD due to PSEN-1-E280A variant can be trained using spectral features, and the importance of the beta band and precuneus region is highlighted in asymptomatic stages, opening up the possibility of its use as a screening methodology.
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Affiliation(s)
- Francisco J. García Pretelt
- Bioinstrumentation and Clinical Engineering Research Group (GIBIC), Bioengineering Program, Universidad de Antioquia, Medellín, Colombia
- Neuropsychology and Behavior Group (GRUNECO), Medical School, Universidad de Antioquia, Medellín, Colombia
| | - Jazmín X. Suárez Relevo
- Bioinstrumentation and Clinical Engineering Research Group (GIBIC), Bioengineering Program, Universidad de Antioquia, Medellín, Colombia
- Neuropsychology and Behavior Group (GRUNECO), Medical School, Universidad de Antioquia, Medellín, Colombia
| | - David Aguillón
- Neuroscience Group of Antioquia (GNA), Medical School, Universidad de Antioquia, Medellín, Colombia
- Neuropsychology and Behavior Group (GRUNECO), Medical School, Universidad de Antioquia, Medellín, Colombia
| | - Francisco Lopera
- Neuroscience Group of Antioquia (GNA), Medical School, Universidad de Antioquia, Medellín, Colombia
| | - John Fredy Ochoa
- Bioinstrumentation and Clinical Engineering Research Group (GIBIC), Bioengineering Program, Universidad de Antioquia, Medellín, Colombia
- Neuropsychology and Behavior Group (GRUNECO), Medical School, Universidad de Antioquia, Medellín, Colombia
| | - Carlos A. Tobón Quintero
- Neuroscience Group of Antioquia (GNA), Medical School, Universidad de Antioquia, Medellín, Colombia
- Neuropsychology and Behavior Group (GRUNECO), Medical School, Universidad de Antioquia, Medellín, Colombia
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10
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Machine learning techniques for diagnosis of alzheimer disease, mild cognitive disorder, and other types of dementia. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103293] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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11
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Prado P, Birba A, Cruzat J, Santamaría-García H, Parra M, Moguilner S, Tagliazucchi E, Ibáñez A. Dementia ConnEEGtome: Towards multicentric harmonization of EEG connectivity in neurodegeneration. Int J Psychophysiol 2022; 172:24-38. [PMID: 34968581 PMCID: PMC9887537 DOI: 10.1016/j.ijpsycho.2021.12.008] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 11/26/2021] [Accepted: 12/19/2021] [Indexed: 02/02/2023]
Abstract
The proposal to use brain connectivity as a biomarker for dementia phenotyping can be potentiated by conducting large-scale multicentric studies using high-density electroencephalography (hd- EEG). Nevertheless, several barriers preclude the development of a systematic "ConnEEGtome" in dementia research. Here we review critical sources of variability in EEG connectivity studies, and provide general guidelines for multicentric protocol harmonization. We describe how results can be impacted by the choice for data acquisition, and signal processing workflows. The implementation of a particular processing pipeline is conditional upon assumptions made by researchers about the nature of EEG. Due to these assumptions, EEG connectivity metrics are typically applicable to restricted scenarios, e.g., to a particular neurocognitive disorder. "Ground truths" for the choice of processing workflow and connectivity analysis are impractical. Consequently, efforts should be directed to harmonizing experimental procedures, data acquisition, and the first steps of the preprocessing pipeline. Conducting multiple analyses of the same data and a proper integration of the results need to be considered in additional processing steps. Furthermore, instead of using a single connectivity measure, using a composite metric combining different connectivity measures brings a powerful strategy to scale up the replicability of multicentric EEG connectivity studies. These composite metrics can boost the predictive strength of diagnostic tools for dementia. Moreover, the implementation of multi-feature machine learning classification systems that include EEG-based connectivity analyses may help to exploit the potential of multicentric studies combining clinical-cognitive, molecular, genetics, and neuroimaging data towards a multi-dimensional characterization of the dementia.
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Affiliation(s)
- Pavel Prado
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile
| | - Agustina Birba
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile,Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina,National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
| | - Josefina Cruzat
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile
| | - Hernando Santamaría-García
- Pontificia Universidad Javeriana, Medical School, Physiology and Psychiatry Departments, Memory and Cognition Center Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia
| | - Mario Parra
- School of Psychological Sciences and Health, University of Strathclyde, Glasgow, United Kingdom
| | - Sebastian Moguilner
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile,Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina,Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), California, USA,Trinity College Dublin (TCD), Dublin, Ireland
| | - Enzo Tagliazucchi
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile,Departamento de Física, Universidad de Buenos Aires and Instituto de Fisica de Buenos Aires (IFIBA -CONICET), Buenos Aires, Argentina
| | - Agustín Ibáñez
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile,Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina,National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina,Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), California, USA,Trinity College Dublin (TCD), Dublin, Ireland,Corresponding author at: Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile., (A. Ibáñez)
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12
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Mahendran N, Vincent PMDR, Srinivasan K, Chang CY. Improving the Classification of Alzheimer's Disease Using Hybrid Gene Selection Pipeline and Deep Learning. Front Genet 2021; 12:784814. [PMID: 34868275 PMCID: PMC8632950 DOI: 10.3389/fgene.2021.784814] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 10/20/2021] [Indexed: 11/13/2022] Open
Abstract
Alzheimer’s is a progressive, irreversible, neurodegenerative brain disease. Even with prominent symptoms, it takes years to notice, decode, and reveal Alzheimer’s. However, advancements in technologies, such as imaging techniques, help in early diagnosis. Still, sometimes the results are inaccurate, which delays the treatment. Thus, the research in recent times focused on identifying the molecular biomarkers that differentiate the genotype and phenotype characteristics. However, the gene expression dataset’s generated features are huge, 1,000 or even more than 10,000. To overcome such a curse of dimensionality, feature selection techniques are introduced. We designed a gene selection pipeline combining a filter, wrapper, and unsupervised method to select the relevant genes. We combined the minimum Redundancy and maximum Relevance (mRmR), Wrapper-based Particle Swarm Optimization (WPSO), and Auto encoder to select the relevant features. We used the GSE5281 Alzheimer’s dataset from the Gene Expression Omnibus We implemented an Improved Deep Belief Network (IDBN) with simple stopping criteria after choosing the relevant genes. We used a Bayesian Optimization technique to tune the hyperparameters in the Improved Deep Belief Network. The tabulated results show that the proposed pipeline shows promising results.
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Affiliation(s)
- Nivedhitha Mahendran
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - P M Durai Raj Vincent
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Kathiravan Srinivasan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
| | - Chuan-Yu Chang
- Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan
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Karami V, Nittari G, Traini E, Amenta F. An Optimized Decision Tree with Genetic Algorithm Rule-Based Approach to Reveal the Brain's Changes During Alzheimer's Disease Dementia. J Alzheimers Dis 2021; 84:1577-1584. [PMID: 34719494 DOI: 10.3233/jad-210626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND It is desirable to achieve acceptable accuracy for computer aided diagnosis system (CADS) to disclose the dementia-related consequences on the brain. Therefore, assessing and measuring these impacts is fundamental in the diagnosis of dementia. OBJECTIVE This study introduces a new CADS for deep learning of magnetic resonance image (MRI) data to identify changes in the brain during Alzheimer's disease (AD) dementia. METHODS The proposed algorithm employed a decision tree with genetic algorithm rule-based optimization to classify input data which were extracted from MRI. This pipeline is applied to the healthy and AD subjects of the Open Access Series of Imaging Studies (OASIS). RESULTS Final evaluation of the CADS and its comparison with other systems supported the potential of the proposed model as a novel tool for investigating the progression of AD and its great ability as an innovative computerized help to facilitate the decision-making procedure for the diagnosis of AD. CONCLUSION The one-second time response, together with the identified high accurate performance, suggests that this system could be useful in future cognitive and computational neuroscience studies.
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Affiliation(s)
- Vania Karami
- Department of Neurology and Neurosurgery, MontrealNeurological Institute-Hospital (MNI), McGill University, Montreal, Canada
| | - Giulio Nittari
- School of PharmaceuticalSciences and Health Products, University of Camerino, Camerino (MC), Italy
| | - Enea Traini
- School of PharmaceuticalSciences and Health Products, University of Camerino, Camerino (MC), Italy
| | - Francesco Amenta
- School of PharmaceuticalSciences and Health Products, University of Camerino, Camerino (MC), Italy
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14
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Kaplan E, Dogan S, Tuncer T, Baygin M, Altunisik E. Feed-forward LPQNet based Automatic Alzheimer's Disease Detection Model. Comput Biol Med 2021; 137:104828. [PMID: 34507154 DOI: 10.1016/j.compbiomed.2021.104828] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 08/16/2021] [Accepted: 09/01/2021] [Indexed: 12/19/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) is one of the most commonly seen brain ailments worldwide. Therefore, many researches have been presented about AD detection and cure. In addition, machine learning models have also been proposed to detect AD promptly. MATERIALS AND METHOD In this work, a new brain image dataset was collected. This dataset contains two categories, and these categories are healthy and AD. This dataset was collected from 1070 subjects. This work presents an automatic AD detection model to detect AD using brain images automatically. The presented model is called a feed-forward local phase quantization network (LPQNet). LPQNet consists of (i) multilevel feature generation based on LPQ and average pooling, (ii) feature selection using neighborhood component analysis (NCA), and (iii) classification phases. The prime objective of the presented LPQNet is to reach high accuracy with low computational complexity. LPQNet generates features on six levels. Therefore, 256 × 6 = 1536 features are generated from an image, and the most important 256 out 1536 features are selected. The selected 256 features are classified on the conventional classifiers to denote the classification capability of the generated and selected features by LPQNet. RESULTS The presented LPQNet was tested on three image datasets to demonstrate the universal classification ability of the LPQNet. The proposed LPQNet attained 99.68%, 100%, and 99.64% classification accuracy on the collected AD image dataset, the Harvard Brain Atlas AD dataset, and the Kaggle AD dataset. Moreover, LPQNet attained 99.62% accuracy on the Kaggle AD dataset using four classes. CONCLUSIONS Moreover, the calculated results from LPQNet are compared to other automatic AD detection models. Comparisons, results, and findings clearly denote the superiority of the presented model. In addition, a new intelligent AD detector application can be developed for use in magnetic resonance (MR) and computed tomography (CT) devices. By using the developed automated AD detector, new generation intelligence MR and CT devices can be developed.
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Affiliation(s)
- Ela Kaplan
- Department of Radiology, Adiyaman Training and Research Hospital, Adiyaman, Turkey.
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey.
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey.
| | - Mehmet Baygin
- Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan, Turkey.
| | - Erman Altunisik
- Department of Neurology, Adiyaman University Medicine Faculty, Adiyaman, Turkey.
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Logan R, Williams BG, Ferreira da Silva M, Indani A, Schcolnicov N, Ganguly A, Miller SJ. Deep Convolutional Neural Networks With Ensemble Learning and Generative Adversarial Networks for Alzheimer's Disease Image Data Classification. Front Aging Neurosci 2021; 13:720226. [PMID: 34483890 PMCID: PMC8416107 DOI: 10.3389/fnagi.2021.720226] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 07/15/2021] [Indexed: 11/26/2022] Open
Abstract
Recent advancements in deep learning (DL) have made possible new methodologies for analyzing massive datasets with intriguing implications in healthcare. Convolutional neural networks (CNN), which have proven to be successful supervised algorithms for classifying imaging data, are of particular interest in the neuroscience community for their utility in the classification of Alzheimer's disease (AD). AD is the leading cause of dementia in the aging population. There remains a critical unmet need for early detection of AD pathogenesis based on non-invasive neuroimaging techniques, such as magnetic resonance imaging (MRI) and positron emission tomography (PET). In this comprehensive review, we explore potential interdisciplinary approaches for early detection and provide insight into recent advances on AD classification using 3D CNN architectures for multi-modal PET/MRI data. We also consider the application of generative adversarial networks (GANs) to overcome pitfalls associated with limited data. Finally, we discuss increasing the robustness of CNNs by combining them with ensemble learning (EL).
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Affiliation(s)
- Robert Logan
- Pluripotent Diagnostics Corp. (PDx), Molecular Medicine Research Institute, Sunnyvale, CA, United States
- Eastern Nazarene College, Quincy, MA, United States
| | - Brian G. Williams
- Pluripotent Diagnostics Corp. (PDx), Molecular Medicine Research Institute, Sunnyvale, CA, United States
| | - Maria Ferreira da Silva
- Pluripotent Diagnostics Corp. (PDx), Molecular Medicine Research Institute, Sunnyvale, CA, United States
| | - Akash Indani
- Pluripotent Diagnostics Corp. (PDx), Molecular Medicine Research Institute, Sunnyvale, CA, United States
| | - Nicolas Schcolnicov
- Pluripotent Diagnostics Corp. (PDx), Molecular Medicine Research Institute, Sunnyvale, CA, United States
| | - Anjali Ganguly
- Pluripotent Diagnostics Corp. (PDx), Molecular Medicine Research Institute, Sunnyvale, CA, United States
| | - Sean J. Miller
- Pluripotent Diagnostics Corp. (PDx), Molecular Medicine Research Institute, Sunnyvale, CA, United States
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16
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Amini M, Pedram MM, Moradi A, Ouchani M. Diagnosis of Alzheimer's Disease by Time-Dependent Power Spectrum Descriptors and Convolutional Neural Network Using EEG Signal. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:5511922. [PMID: 33981355 PMCID: PMC8088352 DOI: 10.1155/2021/5511922] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 03/26/2021] [Accepted: 04/07/2021] [Indexed: 12/22/2022]
Abstract
Using strategies that obtain biomarkers where early symptoms coincide, the early detection of Alzheimer's disease and its complications is essential. Electroencephalogram is a technology that allows thousands of neurons with equal spatial orientation of the duration of cerebral cortex electrical activity to be registered by postsynaptic potential. Therefore, in this paper, the time-dependent power spectrum descriptors are used to diagnose the electroencephalogram signal function from three groups: mild cognitive impairment, Alzheimer's disease, and healthy control test samples. The final feature used in three modes of traditional classification methods is recorded: k-nearest neighbors, support vector machine, linear discriminant analysis approaches, and documented results. Finally, for Alzheimer's disease patient classification, the convolutional neural network architecture is presented. The results are indicated using output assessment. For the convolutional neural network approach, the accurate meaning of accuracy is 82.3%. 85% of mild cognitive impairment cases are accurately detected in-depth, but 89.1% of the Alzheimer's disease and 75% of the healthy population are correctly diagnosed. The presented convolutional neural network outperforms other approaches because performance and the k-nearest neighbors' approach is the next target. The linear discriminant analysis and support vector machine were at the low area under the curve values.
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Affiliation(s)
- Morteza Amini
- Department of Cognitive Modeling, Institute for Cognitive Science Studies, Shahid Beheshti University, Tehran, Iran
| | - Mir Mohsen Pedram
- Department of Electrical and Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
- Department of Cognitive Modeling, Institute for Cognitive Science Studies, Tehran, Iran
| | - AliReza Moradi
- Department of Clinical Psychology, Faculty of Psychology and Educational Science, Kharazmi University, Tehran, Iran
- Department of Cognitive Psychology, Institute for Cognitive Science Studies, Tehran, Iran
| | - Mahshad Ouchani
- Institute for Cognitive and Brain Science, Shahid Beheshti University, Tehran, Iran
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17
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The promise of artificial neural networks, EEG, and MRI for Alzheimer's disease. Clin Neurophysiol 2020; 132:207-209. [PMID: 33176985 DOI: 10.1016/j.clinph.2020.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 10/20/2020] [Indexed: 11/24/2022]
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